Who is this presentation for?

Prerequisite knowledge

What you'll learn

Learn how to forecast and interpret intermittent product demand and how to determine the optimal inventory level using predicted demand

Understand in which scenarios are methods such as Croston applicable

Description

Intermittent demand—when a product or SKU experiences several periods of zero demand—is highly variable. Intermittent demand is very common in industries such as aviation, automotive, defense, manufacturing, and retail. It also typically occurs with products nearing the end of their lifecycle.

However, due to the many zero values in intermittent demand time series, the usual methods of forecasting, such as exponential smoothing and ARIMA, do not give an accurate forecast. In these cases, approaches such as Croston may provide a better accuracy over traditional methods. Prateek Nagaria compares traditional and Croston methods in R on intermittent demand time series.

Topics include:

The differences between traditional forecasting and intermittent forecasting

A brief overview of different methods for intermittent demand forecasting

Applying and interpreting the Croston method

A comparison between traditional methods and the Croston method on real-world SKUs data in R

Prateek Nagaria

The Data Team

Prateek Nagaria is a data scientist for the Data Team. Prateek is an advanced analytics expert with more than five years of experience. He specializes in business analytics, big data technologies, and statistical modeling as well as programming languages like R, Python, Java, C, C++. Prateek holds a master’s degree in enterprise business analytics from the National University of Singapore and a bachelor’s degree in computer science and engineering.